Detecting and Optimising Team Interactions in Software Development
- URL: http://arxiv.org/abs/2302.14609v1
- Date: Tue, 28 Feb 2023 14:53:29 GMT
- Title: Detecting and Optimising Team Interactions in Software Development
- Abstract summary: This paper presents a data-driven approach to detect the functional interaction structure for software development teams.
Our approach considers differences in the activity levels of team members and uses a block-constrained configuration model.
We show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios.
- Score: 58.720142291102135
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The functional interaction structure of a team captures the preferences with
which members of different roles interact. This paper presents a data-driven
approach to detect the functional interaction structure for software
development teams from traces team members leave on development platforms
during their daily work. Our approach considers differences in the activity
levels of team members and uses a block-constrained configuration model to
compute interaction preferences between members of different roles. We apply
our approach in a case study to extract the functional interaction structure of
a product team at the German IT security company genua GmbH. We subsequently
validate the accuracy of the detected interaction structure in interviews with
five team members. Finally, we show how our approach enables teams to compare
their functional interaction structure against synthetically created benchmark
scenarios. Specifically, we evaluate the level of knowledge diffusion in the
team and identify areas where the team can further improve. Our approach is
computationally efficient and can be applied in real time to manage a team's
interaction structure.
Related papers
- Towards Actionable Surgical Team Dynamics: from Teamwork to Counterfactual Annotations [8.839435095280962]
We introduce an extended multimodal dataset for surgical team interaction analysis, built from real operating room recordings.<n>Team performance is assessed using a standardized surgical evaluation protocol, while interaction quality and individual attributes are annotated.<n>To further support the study of coordination breakdowns and performance variability, we introduce counterfactual annotations.<n>The dataset is designed to support the study of how individual actions, interaction patterns, and team-level processes jointly contribute to team outcomes in surgical settings.
arXiv Detail & Related papers (2026-08-24T14:55:18Z) - Searching for Synergy in Shared Workspace Human-AI Collaboration [1.4923836729114697]
We use simulated shared-workspace human-AI teams as a testbed for studying how collaboration structure shapes team behavior.<n>We find that adding additional collaborators can lower performance when coordination structure is absent.<n>We then evaluate collaboration scaffolding that combines shared group memory with simulated human-in-the-loop (HITL) gates.
arXiv Detail & Related papers (2026-06-16T19:08:43Z) - VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions [63.13827503828231]
We introduce VitaBench 2.0, a benchmark for evaluating personalized and proactive agent behavior in long-term user interactions.<n>Results show that real-world personalization remains highly challenging even for state-of-the-art models.
arXiv Detail & Related papers (2026-05-26T15:07:38Z) - Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
arXiv Detail & Related papers (2026-05-14T14:36:13Z) - An Edge-Host-Cloud Architecture for Robot-Agnostic, Caregiver-in-the-Loop Personalized Cognitive Exercise: Multi-Site Deployment in Dementia Care [1.4582793306013617]
We present Speaking Memories, a distributed, stakeholder-in-the-loop robotic interaction platform for personalized cognitive exercise support.<n>The platform fuses auditory, visual, and textual signals to enable emotion-aware, personalized dialogue.<n>We evaluate the platform through real-world deployments, measuring end-to-end latency, dialogue coherence, interaction stability, and stakeholder-reported usability and engagement.
arXiv Detail & Related papers (2026-04-01T00:41:20Z) - EmCoop: A Framework and Benchmark for Embodied Cooperation Among LLM Agents [18.802912315746564]
EmCoop is a benchmark framework for studying cooperation in embodied multi-agent systems.<n>Our framework separates a high-level cognitive layer from a low-level embodied interaction layer.<n>We propose generalizable, process-level metrics that diagnose collaboration quality and failure modes.
arXiv Detail & Related papers (2026-02-27T22:28:33Z) - Learning Human-Object Interaction as Groups [52.28258599873394]
GroupHOI is a framework that propagates contextual information in terms of geometric proximity and semantic similarity.<n>It exhibits leading performance on the more challenging Nonverbal Interaction Detection task.
arXiv Detail & Related papers (2025-10-21T07:25:10Z) - OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration [13.032459988986068]
OSC (Orchestrating Cognitive Synergy) is a knowledge-aware adaptive collaboration framework.<n>It enhances cognitive synergy in multi-agent systems with large language models.
arXiv Detail & Related papers (2025-09-05T07:44:05Z) - Dynamic benchmarking framework for LLM-based conversational data capture [0.0]
This paper introduces a benchmarking framework to assess large language models (LLMs)
It integrates generative agent simulation to evaluate performance on key dimensions: information extraction, context awareness, and adaptive engagement.
Results show that adaptive strategies improve data extraction accuracy, especially when handling ambiguous responses.
arXiv Detail & Related papers (2025-02-04T15:47:47Z) - ML-SPEAK: A Theory-Guided Machine Learning Method for Studying and Predicting Conversational Turn-taking Patterns [25.049072387358244]
We develop a computational model of conversational turn-taking within self-organized teams.
By bridging the gap between individual personality traits and team communication patterns, our model has the potential to inform theories of team processes.
arXiv Detail & Related papers (2024-11-23T01:27:01Z) - Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning [10.932974027102619]
This study introduces a computational framework based on multi-agent reinforcement learning in the spatial Prisoner's Dilemma game.
By modelling each agent using two distinct Q-networks, we disentangle the coevolutionary dynamics between cooperation and interaction.
arXiv Detail & Related papers (2024-05-04T12:42:55Z) - Unified Human-Scene Interaction via Prompted Chain-of-Contacts [61.87652569413429]
Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality.
This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands.
arXiv Detail & Related papers (2023-09-14T17:59:49Z) - ProAgent: Building Proactive Cooperative Agents with Large Language
Models [89.53040828210945]
ProAgent is a novel framework that harnesses large language models to create proactive agents.
ProAgent can analyze the present state, and infer the intentions of teammates from observations.
ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
arXiv Detail & Related papers (2023-08-22T10:36:56Z) - Building Cooperative Embodied Agents Modularly with Large Language
Models [104.57849816689559]
We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments.
We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework.
Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
arXiv Detail & Related papers (2023-07-05T17:59:27Z) - Knowledge-based Reasoning and Learning under Partial Observability in Ad
Hoc Teamwork [4.454557728745761]
This paper introduces an architecture that determines an ad hoc agent's behavior based on non-monotonic logical reasoning.
It supports online selection, adaptation, and learning of the models that predict the other agents' behavior.
We show that the performance of our architecture is comparable or better than state of the art data-driven baselines in both simple and complex scenarios.
arXiv Detail & Related papers (2023-06-01T15:21:27Z) - Rethinking Trajectory Prediction via "Team Game" [118.59480535826094]
We present a novel formulation for multi-agent trajectory prediction, which explicitly introduces the concept of interactive group consensus.
On two multi-agent settings, i.e. team sports and pedestrians, the proposed framework consistently achieves superior performance compared to existing methods.
arXiv Detail & Related papers (2022-10-17T07:16:44Z) - Structure Extraction in Task-Oriented Dialogues with Slot Clustering [94.27806592467537]
In task-oriented dialogues, dialogue structure has often been considered as transition graphs among dialogue states.
We propose a simple yet effective approach for structure extraction in task-oriented dialogues.
arXiv Detail & Related papers (2022-02-28T20:18:12Z) - Advancing Methodology for Social Science Research Using Alternate
Reality Games: Proof-of-Concept Through Measuring Individual Differences and
Adaptability and their impact on Team Performance [8.680283375719466]
We discuss work tackling an open problem with a focus on understanding individual differences and its effect on team adaptation.
We specifically discuss our contribution in terms of methods that augment survey data and behavioral data.
To make this problem more tractable we chose to focus on specific types of environments, Alternate Reality Games (ARGs)
arXiv Detail & Related papers (2021-06-25T16:22:22Z) - Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning [11.480994804659908]
We build on graph neural networks to learn agent models and joint-action value models under varying team compositions.
We empirically demonstrate that our approach successfully models the effects other agents have on the learner, leading to policies that robustly adapt to dynamic team compositions.
arXiv Detail & Related papers (2020-06-18T10:39:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.